Usage

`javascript
var dsquaredEuclidean = require( '@stdlib/stats-strided-distances-dsquared-euclidean' );
`

#### dsquaredEuclidean( N, x, strideX, y, strideY )

Computes the squared Euclidean distance between two double-precision floating-point strided arrays.

`javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var y = new Float64Array( [ 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 ] );

var z = dsquaredEuclidean( x.length, x, 1, y, 1 );
// returns 72.0
`

The function has the following parameters:

- N: number of indexed elements.
- x: input [Float64Array][@stdlib/array/float64].
- strideX: stride length of x.
- y: input [Float64Array][@stdlib/array/float64].
- strideY: stride length of y.

The N and stride parameters determine which elements in the strided arrays are accessed at runtime. For example, to calculate the squared Euclidean distance between every other element in x and the first N elements of y in reverse order,

`javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] );

var z = dsquaredEuclidean( 3, x, 2, y, -1 );
// returns 20.0
`

Note that indexing is relative to the first index. To introduce an offset, use [typed array][mdn-typed-array] views.

`javascript
var Float64Array = require( '@stdlib/array-float64' );

// Initial arrays...
var x0 = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y0 = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

// Create offset views...
var x1 = new Float64Array( x0.buffer, x0.BYTES_PER_ELEMENT*1 ); // start at 2nd element
var y1 = new Float64Array( y0.buffer, y0.BYTES_PER_ELEMENT*3 ); // start at 4th element

var z = dsquaredEuclidean( 3, x1, 1, y1, 1 );
// returns 192.0
`

#### dsquaredEuclidean.ndarray( N, x, strideX, offsetX, y, strideY, offsetY )

Computes the squared Euclidean distance between two double-precision floating-point strided arrays using alternative indexing semantics.

`javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 2.0, -7.0, -2.0, 3.0, 4.0, 2.0 ] );
var y = new Float64Array( [ 2.0, 1.0, 2.0, 1.0, -2.0, 2.0, 3.0, 4.0 ] );

var z = dsquaredEuclidean.ndarray( x.length, x, 1, 0, y, 1, 0 );
// returns 72.0
`

The function has the following additional parameters:

- offsetX: starting index for x.
- offsetY: starting index for y.

While [typed array][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example, to calculate the squared Euclidean distance between every other element in x starting from the second element with the last 3 elements in y in reverse order

`javascript
var Float64Array = require( '@stdlib/array-float64' );

var x = new Float64Array( [ 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 ] );
var y = new Float64Array( [ 7.0, 8.0, 9.0, 10.0, 11.0, 12.0 ] );

var z = dsquaredEuclidean.ndarray( 3, x, 2, 1, y, -1, y.length-1 );
// returns 165.0
`